A Monte Carlo study of IRTree models' ability to recover item parameters.

Item response tree (IRTree) models are theorized to extract response styles from self-report data by utilizing multidimensional item response theory (IRT) models based on theoretical decision processes. Despite the growing popularity of the IRTree framework, there has been little research that has systematically examined the ability of its most popular models to recover item parameters across sample size and test length. This Monte Carlo simulation study explored the ability of IRTree models to recover item parameters based on data created from the midpoint primary process model.